Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators
نویسندگان
چکیده
منابع مشابه
Highly Scalable Attribute Selection for Averaged One-Dependence Estimators
Averaged One-Dependence Estimators (AODE) is a popular and effective approach to Bayesian learning. In this paper, a new attribute selection approach is proposed for AODE. It can search in a large model space, while it requires only a single extra pass through the training data, resulting in a computationally efficient two-pass learning algorithm. The experimental results indicate that the new ...
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Naive Bayes is a simple, computationally efficient and remarkably accurate approach to classification learning. These properties have led to its wide deployment in many online applications. However, it is based on an assumption that all attributes are conditionally independent given the class. This assumption leads to decreased accuracy in some applications. AODE overcomes the attribute indepen...
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http://dx.doi.org/10.1016/j.eswa.2014.09.019 0957-4174/ 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel./fax: +86 27 67883714. E-mail addresses: [email protected] (J. Wu), [email protected]. edu.au (S. Pan), [email protected] (X. Zhu), [email protected] (Z. Cai), peng.zhang@uts. edu.au (P. Zhang), [email protected] (C. Zhang). Jia Wu , Shirui Pan , Xingquan Zh...
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Averaged One-Dependence Estimators (AODE) classifies by uniformly aggregating all qualified one-dependence estimators (ODEs). Its capacity to significantly improve naive Bayes’ accuracy without undue time complexity has attracted substantial interest. Forward Sequential Selection and Backwards Sequential Elimination are effective wrapper techniques to identify and repair harmful interdependenci...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2971706